What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Topology
His main research concerns Motion planning, Artificial intelligence, Robot, Mathematical optimization and Heuristic.
His Motion planning research is multidisciplinary, relying on both Homotopy, Graph and Trajectory.
His Artificial intelligence research incorporates elements of Best-first search, Iterative deepening depth-first search, Incremental heuristic search, Beam search and Task.
His Robot study combines topics from a wide range of disciplines, such as Real-time computing and Graph.
His research in Mathematical optimization intersects with topics in Any-angle path planning, Monotone polygon and Range.
His Heuristic research incorporates themes from Humanoid robot and Heuristics.
His most cited work include:
- Autonomous driving in urban environments: Boss and the Urban Challenge (1101 citations)
- ARA*: Anytime A* with Provable Bounds on Sub-Optimality (451 citations)
- Anytime dynamic A*: an anytime, replanning algorithm (442 citations)
What are the main themes of his work throughout his whole career to date?
Maxim Likhachev mainly investigates Motion planning, Robot, Artificial intelligence, Mathematical optimization and Heuristic.
The concepts of his Motion planning study are interwoven with issues in Motion, Trajectory and Mobile robot.
His Robot research focuses on Human–computer interaction and how it connects with Focus.
He interconnects Machine learning, Task and Computer vision in the investigation of issues within Artificial intelligence.
His work in Mathematical optimization addresses subjects such as Graph, which are connected to disciplines such as Graph theory.
His Heuristic research incorporates themes from Beam search, Humanoid robot and Heuristics.
He most often published in these fields:
- Motion planning (48.48%)
- Robot (44.95%)
- Artificial intelligence (37.37%)
What were the highlights of his more recent work (between 2018-2021)?
- Motion planning (48.48%)
- Robot (44.95%)
- Mathematical optimization (30.81%)
In recent papers he was focusing on the following fields of study:
Maxim Likhachev mostly deals with Motion planning, Robot, Mathematical optimization, Distributed computing and State.
His Motion planning research is classified as research in Path.
His research in Robot intersects with topics in Object, Human–computer interaction, Task and Maxima and minima.
In general Mathematical optimization study, his work on Heuristic and Optimal planning often relates to the realm of Quality and Observable, thereby connecting several areas of interest.
The concepts of his Distributed computing study are interwoven with issues in Reinforcement learning, Loop, Robotic arm and Model free.
His work carried out in the field of State brings together such families of science as Function, Collision, Control theory and Perspective.
Between 2018 and 2021, his most popular works were:
- POMHDP: Search-Based Belief Space Planning Using Multiple Heuristics (9 citations)
- Driving in Dense Traffic with Model-Free Reinforcement Learning (8 citations)
- Planning and Execution using Inaccurate Models with Provable Guarantees (7 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Topology
Maxim Likhachev focuses on Motion planning, Distributed computing, Robot, Model free and Reinforcement learning.
His research investigates the connection between Motion planning and topics such as Algorithm that intersect with problems in Heuristic.
His work deals with themes such as Loop, Task, Speedup and Behavior-based robotics, which intersect with Distributed computing.
The various areas that he examines in his Robot study include Object, State and Face.
Maxim Likhachev combines subjects such as Motion and Real-time computing with his study of Object.
His studies in Model free integrate themes in fields like Task analysis and Operations research.
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